hpgltools Differential Expression Analyses Using the Fission Dataset

1 Example hpgltool usage with a real data set (fission)

This document aims to provide further examples in how to use the hpgltools.

Note to self, the header has rmarkdown::pdf_document instead of html_document or html_vignette because it gets some bullcrap error ‘margins too large’…

1.1 Setting up

Here are the commands I invoke to get ready to play with new data, including everything required to install hpgltools, the software it uses, and the fission data.

library(hpgltools)
tt <- sm(library(fission))
tt <- data(fission)

1.2 Annotation collection

Later on in this, I will do some ontology shenanigans. But I can grab some annotations from biomart now.

pombe_annotations <- load_biomart_annotations(
    host = "fungi.ensembl.org",
    trymart = "fungal_mart",
    trydataset = "spombe_eg_gene",
    gene_requests = c("pombase_transcript", "ensembl_gene_id", "ensembl_transcript_id",
                      "hgnc_symbol", "description", "gene_biotype"),
    species = "spombe", overwrite = TRUE)

## Unable to perform useMart, perhaps the host/mart is incorrect: fungi.ensembl.org fungal_mart.

## The available marts are:

## fungi_mart, fungi_variations

## Trying the first one.

## Successfully connected to the spombe_eg_gene database.

## Some attributes in your request list were not in the ensembl database.

## Finished downloading ensembl gene annotations.

## Finished downloading ensembl structure annotations.

## Dropping haplotype chromosome annotations, set drop_haplotypes = FALSE if this is bad.

## Saving annotations to spombe_biomart_annotations.rda.

## Finished save().

pombe_mart <- pombe_annotations[["mart"]]
annotations <- pombe_annotations[["annotation"]]
rownames(annotations) <- make.names(gsub(pattern = "\\.\\d+$",
                                         replacement = "",
                                         x = rownames(annotations)), unique = TRUE)

1.3 Data import

All the work I do in Dr. El-Sayed’s lab makes some pretty hard assumptions about how data is stored. As a result, to use the fission data set I will do a little bit of shenanigans to match it to the expected format. Now that I have played a little with fission, I think its format is quite nice and am likely to have my experiment class instead be a SummarizedExperiment.

## Extract the meta data from the fission dataset
meta <- as.data.frame(fission@colData)
## Make conditions and batches
meta[["condition"]] <- paste(meta$strain, meta$minute, sep = ".")
meta[["batch"]] <- meta[["replicate"]]
meta[["sample.id"]] <- rownames(meta)
## Grab the count data
fission_data <- fission@assays[["data"]][["counts"]]
## This will make an experiment superclass called 'expt' and it contains
## an ExpressionSet along with any arbitrary additional information one might want to include.
## Along the way it writes a Rdata file which is by default called 'expt.Rdata'
fission_expt <- create_expt(metadata = meta,
                            count_dataframe = fission_data,
                            gene_info = annotations)

## Reading the sample metadata.

## The sample definitions comprises: 36 rows(samples) and 7 columns(metadata fields).

## Matched 5710 annotations and counts.

## Bringing together the count matrix and gene information.

## Some annotations were lost in merging, setting them to 'undefined'.

## Saving the expressionset to 'expt.rda'.

## The final expressionset has 7039 rows and 36 columns.

2 Some simple differential expression analyses

Travis wisely imposes a limit on the amount of time for building vignettes. My tools by default will attempt all possible pairwise comparisons, which takes a long time. Therefore I am going to take a subset of the data and limit these comparisons to that.

fun_data <- subset_expt(fission_expt,
                        subset = "condition=='wt.120'|condition=='wt.30'")

## There were 36, now there are 6 samples.

fun_filt <- normalize_expt(fun_data, filter = "simple")

## Removing 462 low-count genes (6577 remaining).

fun_norm <- sm(normalize_expt(fun_filt, batch = "limma", norm = "quant",
                              transform = "log2", convert = "cpm"))

2.1 Try using limma first

limma_comparison <- sm(limma_pairwise(fun_data))

names(limma_comparison[["all_tables"]])

## [1] "wt30_vs_wt120"

summary(limma_comparison[["all_tables"]][["wt30_vs_wt120"]])

##      logFC           AveExpr            t             P.Value      
##  Min.   :-4.278   Min.   :-4.58   Min.   :-88.48   Min.   :0.0000  
##  1st Qu.:-0.399   1st Qu.: 1.11   1st Qu.: -2.60   1st Qu.:0.0192  
##  Median :-0.020   Median : 3.97   Median : -0.13   Median :0.1240  
##  Mean   : 0.008   Mean   : 3.11   Mean   : -0.17   Mean   :0.2792  
##  3rd Qu.: 0.300   3rd Qu.: 5.44   3rd Qu.:  1.72   3rd Qu.:0.4653  
##  Max.   : 7.075   Max.   :18.59   Max.   : 62.44   Max.   :1.0000  
##    adj.P.Val            B        
##  Min.   :0.0170   Min.   :-8.29  
##  1st Qu.:0.0767   1st Qu.:-6.58  
##  Median :0.2479   Median :-5.50  
##  Mean   :0.3686   Mean   :-4.87  
##  3rd Qu.:0.6204   3rd Qu.:-3.50  
##  Max.   :1.0000   Max.   : 4.83

scatter_wt_mut <- extract_coefficient_scatter(limma_comparison, type = "limma",
                                              x = "wt30", y = "wt120")

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30

## Actually comparing wt30 and wt120.

scatter_wt_mut[["scatter"]]

scatter_wt_mut[["both_histogram"]][["plot"]] +
  ggplot2::scale_y_continuous(limits = c(0, 0.20))

## Warning: Removed 7039 rows containing non-finite values (stat_bin).

## Warning: Removed 7039 rows containing non-finite values (stat_density).

## Warning: Removed 1 rows containing missing values (geom_vline).

ma_wt_mut <- extract_de_plots(limma_comparison, type = "limma")
ma_wt_mut[["ma"]][["plot"]]

ma_wt_mut[["volcano"]][["plot"]]

2.2 Then DESeq2

deseq_comparison <- sm(deseq2_pairwise(fun_data))

summary(deseq_comparison[["all_tables"]][["wt30_vs_wt120"]])

##     baseMean           logFC            lfcSE            stat        
##  Min.   :      0   Min.   :-5.615   Min.   :0.000   Min.   :-20.800  
##  1st Qu.:     28   1st Qu.:-0.386   1st Qu.:0.168   1st Qu.: -1.176  
##  Median :    192   Median : 0.000   Median :0.222   Median :  0.000  
##  Mean   :   1703   Mean   : 0.020   Mean   :0.489   Mean   :  0.168  
##  3rd Qu.:    536   3rd Qu.: 0.343   3rd Qu.:0.412   3rd Qu.:  1.108  
##  Max.   :4924000   Max.   : 7.212   Max.   :4.072   Max.   : 30.370  
##     P.Value         adj.P.Val     
##  Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0197   1st Qu.:0.0685  
##  Median :0.2503   Median :0.4676  
##  Mean   :0.3600   Mean   :0.4805  
##  3rd Qu.:0.6666   3rd Qu.:0.8732  
##  Max.   :1.0000   Max.   :1.0000

scatter_wt_mut <- extract_coefficient_scatter(deseq_comparison, type = "deseq",
                                              x = "wt30", y = "wt120", gvis_filename = NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30, r2, r3

## Actually comparing wt30 and wt120.

scatter_wt_mut[["scatter"]]

plots_wt_mut <- extract_de_plots(deseq_comparison, type = "deseq")
plots_wt_mut[["ma"]][["plot"]]

plots_wt_mut[["volcano"]][["plot"]]

2.3 EdgeR

edger_comparison <- sm(edger_pairwise(fun_data, model_batch = TRUE))

plots_wt_mut <- extract_de_plots(edger_comparison, type = "edger")
scatter_wt_mut <- extract_coefficient_scatter(edger_comparison, type = "edger",
                                              x = "wt30", y = "wt120", gvis_filename = NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30

## Actually comparing wt30 and wt120.

scatter_wt_mut[["scatter"]]

plots_wt_mut[["ma"]][["plot"]]

plots_wt_mut[["volcano"]][["plot"]]

2.4 EBSeq

{r simple_edger2 ebseq_comparison <- sm(ebseq_pairwise(fun_data)) head(ebseq_comparison$all_tables[[1]])

2.5 My stupid basic comparison

basic_comparison <- sm(basic_pairwise(fun_data))
summary(basic_comparison$all_tables$wt30_vs_wt120)

##  numerator_mean  denominator_mean numerator_var   denominator_var
##  Min.   :-2.20   Min.   :-3.20    Min.   :0.000   Min.   :0.000  
##  1st Qu.: 3.29   1st Qu.: 3.29    1st Qu.:0.019   1st Qu.:0.010  
##  Median : 4.63   Median : 4.63    Median :0.053   Median :0.028  
##  Mean   : 4.70   Mean   : 4.70    Mean   :0.134   Mean   :0.073  
##  3rd Qu.: 5.92   3rd Qu.: 5.93    3rd Qu.:0.134   3rd Qu.:0.074  
##  Max.   :18.61   Max.   :18.61    Max.   :9.509   Max.   :5.907  
##        t                p              logFC             adjp       
##  Min.   :-50.21   Min.   :0.0000   Min.   :-4.073   Min.   :0.0045  
##  1st Qu.: -2.10   1st Qu.:0.0327   1st Qu.:-0.399   1st Qu.:0.1307  
##  Median : -0.39   Median :0.1602   Median :-0.077   Median :0.3203  
##  Mean   : -0.16   Mean   :0.2766   Mean   : 0.000   Mean   :0.3876  
##  3rd Qu.:  1.53   3rd Qu.:0.4585   3rd Qu.: 0.278   3rd Qu.:0.6113  
##  Max.   : 49.10   Max.   :0.9996   Max.   : 7.084   Max.   :0.9996

scatter_wt_mut <- extract_coefficient_scatter(basic_comparison, type = "basic",
                                              x = "wt30", y = "wt120")

## This can do comparisons among the following columns in the pairwise result:

## wt120, wt30

## Actually comparing wt30 and wt120.

scatter_wt_mut[["scatter"]]

plots_wt_mut <- extract_de_plots(basic_comparison, type = "basic")
plots_wt_mut[["ma"]][["plot"]]

plots_wt_mut[["volcano"]][["plot"]]

2.6 Combine them all

all_comparisons <- sm(all_pairwise(fun_data, model_batch = TRUE, parallel = FALSE))

all_combined <- sm(combine_de_tables(all_comparisons, excel = FALSE))
head(all_combined$data[[1]], n = 3)

##              ensembltranscriptid pombasetranscript ensemblgeneid
## SPAC1002.01        SPAC1002.01.1     SPAC1002.01.1   SPAC1002.01
## SPAC1002.02        SPAC1002.02.1     SPAC1002.02.1   SPAC1002.02
## SPAC1002.03c      SPAC1002.03c.1    SPAC1002.03c.1  SPAC1002.03c
##                                                                      description
## SPAC1002.01            conserved fungal protein [Source:PomBase;Acc:SPAC1002.01]
## SPAC1002.02                   nucleoporin Pom34 [Source:PomBase;Acc:SPAC1002.02]
## SPAC1002.03c glucosidase II alpha subunit Gls2 [Source:PomBase;Acc:SPAC1002.03c]
##                 genebiotype cdslength chromosomename strand startposition
## SPAC1002.01  protein_coding       540              I      +       1798347
## SPAC1002.02  protein_coding       690              I      +       1799061
## SPAC1002.03c protein_coding      2772              I      -       1799915
##              endposition deseq_logfc deseq_adjp edger_logfc edger_adjp
## SPAC1002.01      1799015    -1.08000     0.3664    -1.05900     0.2201
## SPAC1002.02      1800053    -0.01485     0.9816    -0.02342     1.0000
## SPAC1002.03c     1803141    -0.22760     0.2327    -0.23630     0.1598
##              limma_logfc limma_adjp basic_nummed basic_denmed basic_numvar
## SPAC1002.01     -0.99860    0.16930        0.000        0.000     0.000000
## SPAC1002.02      0.03778    0.99460        2.860        2.856     0.360293
## SPAC1002.03c    -0.33910    0.02432        6.916        7.252     0.002955
##              basic_denvar basic_logfc  basic_t  basic_p basic_adjp
## SPAC1002.01      0.000000    0.000000  0.00000 0.000000    0.00000
## SPAC1002.02      0.028898    0.004047  0.01124 0.991929    0.99611
## SPAC1002.03c     0.001016   -0.336700 -9.25600 0.001969    0.03734
##              deseq_basemean deseq_lfcse deseq_stat deseq_p ebseq_fc ebseq_logfc
## SPAC1002.01           11.15      0.8209   -1.31600  0.1882   0.5391    -0.89135
## SPAC1002.02           87.42      0.3316   -0.04479  0.9643   1.0571     0.08007
## SPAC1002.03c        1621.00      0.1387   -1.64100  0.1008   0.8580    -0.22094
##              ebseq_c1mean ebseq_c2mean ebseq_mean ebseq_var ebseq_postfc
## SPAC1002.01         14.82        7.987      11.41     33.89       0.5554
## SPAC1002.02         86.42       91.351      88.88    631.94       1.0567
## SPAC1002.03c      1763.03     1512.690    1637.86  36877.63       0.8581
##              ebseq_ppee ebseq_ppde ebseq_adjp edger_logcpm edger_lr edger_p
## SPAC1002.01      0.4683    0.53174     0.4683      0.06691 2.745000 0.09757
## SPAC1002.02      0.9177    0.08227     0.9177      2.89400 0.007429 0.93130
## SPAC1002.03c     0.6947    0.30531     0.6947      7.09500 3.399000 0.06522
##              limma_ave  limma_t limma_b limma_p limma_adjp_ihw deseq_adjp_ihw
## SPAC1002.01    -0.1955  -2.8320 -4.0790 0.07147      1.000e+00      1.000e+00
## SPAC1002.02     2.8470   0.1354 -7.4050 0.90140      1.000e+00      8.822e-01
## SPAC1002.03c    7.0770 -12.5400 -0.6495 0.00151      1.709e-02      2.626e-01
##              edger_adjp_ihw ebseq_adjp_ihw basic_adjp_ihw   lfc_meta   lfc_var
## SPAC1002.01       4.739e-01      1.000e+00      0.000e+00 -1.0520000 7.689e-04
## SPAC1002.02       9.141e-01      1.000e+00      1.000e+00  0.0007106 3.191e-03
## SPAC1002.03c      1.334e-01      2.918e-01      5.214e-03 -0.2681000 2.166e-03
##              lfc_varbymed    p_meta     p_var
## SPAC1002.01    -7.305e-04 1.191e-01 3.753e-03
## SPAC1002.02     4.491e+00 9.323e-01 9.899e-04
## SPAC1002.03c   -8.081e-03 5.584e-02 2.531e-03

sig_genes <- sm(extract_significant_genes(all_combined, excel = FALSE))
head(sig_genes$limma$ups[[1]], n = 3)

##               ensembltranscriptid pombasetranscript ensemblgeneid
## SPBC2F12.09c       SPBC2F12.09c.1    SPBC2F12.09c.1  SPBC2F12.09c
## SPAC22A12.17c     SPAC22A12.17c.1   SPAC22A12.17c.1 SPAC22A12.17c
## SPAPB1A11.02       SPAPB1A11.02.1    SPAPB1A11.02.1  SPAPB1A11.02
##                                                                                 description
## SPBC2F12.09c  transcription factor, Atf-CREB family Atf21 [Source:PomBase;Acc:SPBC2F12.09c]
## SPAC22A12.17c      short chain dehydrogenase (predicted) [Source:PomBase;Acc:SPAC22A12.17c]
## SPAPB1A11.02                  esterase/lipase (predicted) [Source:PomBase;Acc:SPAPB1A11.02]
##                  genebiotype cdslength chromosomename strand startposition
## SPBC2F12.09c  protein_coding      1068             II      +       1722231
## SPAC22A12.17c protein_coding       786              I      -       1185837
## SPAPB1A11.02  protein_coding      1020              I      +       2980428
##               endposition deseq_logfc deseq_adjp edger_logfc edger_adjp
## SPBC2F12.09c      1724450       7.212  5.259e-66       7.170 1.264e-180
## SPAC22A12.17c     1189506       5.855  3.969e-19       5.822  3.155e-57
## SPAPB1A11.02      2981839       6.739  1.894e-06       6.483  1.257e-14
##               limma_logfc limma_adjp basic_nummed basic_denmed basic_numvar
## SPBC2F12.09c        7.075    0.01847        6.174      -0.9106      0.02211
## SPAC22A12.17c       5.609    0.02447        9.396       3.6010      0.02236
## SPAPB1A11.02        5.606    0.01696        1.648      -3.1970      0.68686
##               basic_denvar basic_logfc basic_t  basic_p basic_adjp
## SPBC2F12.09c        0.5967       7.084  15.600 0.003004    0.04238
## SPAC22A12.17c       0.9694       5.795  10.080 0.008325    0.06433
## SPAPB1A11.02        0.4981       4.844   7.708 0.001687    0.03455
##               deseq_basemean deseq_lfcse deseq_stat   deseq_p ebseq_fc
## SPBC2F12.09c           443.5      0.4123     17.490 1.667e-68   143.63
## SPAC22A12.17c         4289.0      0.6255      9.360 7.945e-21    52.82
## SPAPB1A11.02            21.2      1.2810      5.259 1.447e-07   103.34
##               ebseq_logfc ebseq_c1mean ebseq_c2mean ebseq_mean ebseq_var
## SPBC2F12.09c        7.166       6.2270       895.83     451.03 2.477e+05
## SPAC22A12.17c       5.723     161.9714      8556.19    4359.08 2.225e+07
## SPAPB1A11.02        6.691       0.4049        42.86      21.63 8.155e+02
##               ebseq_postfc ebseq_ppee ebseq_ppde ebseq_adjp edger_logcpm
## SPBC2F12.09c        132.23          0          1          0       5.2210
## SPAC22A12.17c        52.65          0          1          0       8.4930
## SPAPB1A11.02         45.38          0          0          0       0.9166
##               edger_lr    edger_p limma_ave limma_t limma_b   limma_p
## SPBC2F12.09c    839.00 1.796e-184     2.592   19.36  0.8017 0.0004519
## SPAC22A12.17c   264.90  1.479e-59     6.482   12.49 -0.3953 0.0015260
## SPAPB1A11.02     65.83  4.910e-16    -1.192   27.66  0.2698 0.0001667
##               limma_adjp_ihw deseq_adjp_ihw edger_adjp_ihw ebseq_adjp_ihw
## SPBC2F12.09c       1.386e-02      4.625e-66     1.076e-180      1.000e+00
## SPAC22A12.17c      1.716e-02      3.792e-19      2.516e-57      7.778e-01
## SPAPB1A11.02       1.000e+00      7.571e-06      1.989e-14      0.000e+00
##               basic_adjp_ihw lfc_meta   lfc_var lfc_varbymed    p_meta
## SPBC2F12.09c       1.263e-02    7.152 0.000e+00    0.000e+00 1.506e-04
## SPAC22A12.17c      1.865e-02    5.916 1.123e-01    1.898e-02 5.087e-04
## SPAPB1A11.02       6.937e-01    6.137 5.880e-02    9.581e-03 5.561e-05
##                   p_var
## SPBC2F12.09c  6.807e-08
## SPAC22A12.17c 7.762e-07
## SPAPB1A11.02  9.255e-09

## Here we see that edger and deseq agree the least:
all_comparisons[["comparison"]][["comp"]]

##                wt30_vs_wt120
## limma_vs_deseq        0.9808
## limma_vs_edger        0.9601
## limma_vs_ebseq        0.7944
## limma_vs_basic        0.9977
## deseq_vs_edger        0.9814
## deseq_vs_ebseq        0.8329
## deseq_vs_basic        0.9965
## edger_vs_ebseq        0.9165
## edger_vs_basic        0.9972
## ebseq_vs_basic        0.9950

## And here we can look at the set of 'significant' genes according to various tools:
yeast_sig <- sm(extract_significant_genes(all_combined, excel = FALSE))
yeast_barplots <- sm(significant_barplots(combined = all_combined))
yeast_barplots[["limma"]]

yeast_barplots[["edger"]]

yeast_barplots[["deseq"]]

2.6.1 Setting up

Since I didn’t acquire this data in a ‘normal’ way, I am going to post-generate a gff file which may be used by clusterprofiler, topgo, and gostats.

Therefore, I am going to make use of TxDb to make the requisite gff file.

limma_results <- limma_comparison[["all_tables"]]
## The set of comparisons performed
names(limma_results)

## [1] "wt30_vs_wt120"

table <- limma_results[["wt30_vs_wt120"]]
dim(table)

## [1] 7039    6

gene_names <- rownames(table)

updown_genes <- get_sig_genes(table, p = 0.05, lfc = 0.4, p_column = "P.Value")
tt <- please_install("GenomicFeatures")
tt <- please_install("biomaRt")
available_marts <- biomaRt::listMarts(host = "fungi.ensembl.org")
available_marts

##            biomart                     version
## 1       fungi_mart      Ensembl Fungi Genes 50
## 2 fungi_variations Ensembl Fungi Variations 50

ensembl_mart <- biomaRt::useMart("fungi_mart", host = "fungi.ensembl.org")
available_datasets <- biomaRt::listDatasets(ensembl_mart)
pombe_hit <- grep(pattern = "pombe", x = available_datasets[["description"]])
pombe_name <- available_datasets[pombe_hit, "dataset"]
pombe_mart <- biomaRt::useDataset(pombe_name, mart = ensembl_mart)

pombe_goids <- biomaRt::getBM(attributes = c("pombase_transcript", "go_id"),
                              values = gene_names, mart = pombe_mart)
colnames(pombe_goids) <- c("ID", "GO")

2.6.2 Setting up with hpgltools

The above worked, it provided a table of ID and ontology. It was however a bit fraught. Here is another way.

## In theory, the above should work with a single function call:
pombe_goids_simple <- load_biomart_go(species = "spombe", overwrite = TRUE,
                                      dl_rows = c("pombase_transcript", "go_id"),
                                      host = "fungi.ensembl.org")

## Unable to perform useMart, perhaps the host/mart is incorrect: fungi.ensembl.org ENSEMBL_MART_ENSEMBL.

## The available marts are:

## fungi_mart, fungi_variations

## Trying the first one.

## Unable to perform useMart, perhaps the host/mart is incorrect: fungi.ensembl.org ENSEMBL_MART_ENSEMBL.

## The available marts are:

## fungi_martfungi_variations

## Trying the first one.

## Unable to perform useDataset, perhaps the given dataset is incorrect: spombe_gene_ensembl.

## Trying instead to use the dataset: spombe_eg_gene

## That seems to have worked, extracting the resulting annotations.

## Finished downloading ensembl go annotations, saving to spombe_go_annotations.rda.

## Saving ontologies to spombe_go_annotations.rda.

## Finished save().

head(pombe_goids_simple[["go"]])

##               ID         GO
## 1    SPRRNA.50.1           
## 2 SPNCRNA.1095.1           
## 3   SPAC212.11.1 GO:0000784
## 4   SPAC212.11.1 GO:0005634
## 5   SPAC212.11.1 GO:0000166
## 6   SPAC212.11.1 GO:0005524

head(pombe_goids)

##               ID         GO
## 1    SPRRNA.50.1           
## 2 SPNCRNA.1095.1           
## 3   SPAC212.11.1 GO:0000784
## 4   SPAC212.11.1 GO:0005634
## 5   SPAC212.11.1 GO:0000166
## 6   SPAC212.11.1 GO:0005524

## This used to work, but does so no longer and I do not know why.
## pombe <- sm(GenomicFeatures::makeTxDbFromBiomart(biomart = "fungal_mart",
##                                                  dataset = "spombe_eg_gene",
##                                                  host = "fungi.ensembl.org"))

## I bet I can get all this information from ensembl now.
## This was found at the bottom of: https://www.biostars.org/p/232005/
link <- "ftp://ftp.ensemblgenomes.org/pub/release-34/fungi/gff3/schizosaccharomyces_pombe/Schizosaccharomyces_pombe.ASM294v2.34.gff3.gz"
pombe <- GenomicFeatures::makeTxDbFromGFF(link, format = "gff3", taxonomyId = 4896,
                                          organism = "Schizosaccharomyces pombe")

## Import genomic features from the file as a GRanges object ...

## OK

## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK

pombe_transcripts <- as.data.frame(GenomicFeatures::transcriptsBy(pombe))
lengths <- pombe_transcripts[, c("group_name","width")]
colnames(lengths) <- c("ID","width")
## Something useful I didn't notice before:
## makeTranscriptDbFromGFF()  ## From GenomicFeatures, much like my own gff2df()
gff_from_txdb <- GenomicFeatures::asGFF(pombe)
## why is GeneID: getting prefixed to the IDs!?
gff_from_txdb$ID <- gsub(x = gff_from_txdb$ID, pattern = "GeneID:", replacement = "")
written_gff <- rtracklayer::export.gff3(gff_from_txdb, con = "pombe.gff")

## Warning in .local(object, con, format, ...): The phase information is missing. The written file will contain CDS
##   with no phase information.

2.7 GOSeq test

summary(updown_genes)

##            Length Class      Mode
## up_genes   6      data.frame list
## down_genes 6      data.frame list

test_genes <- updown_genes[["down_genes"]]
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
lengths[["ID"]] <- paste0(lengths[["ID"]], ".1")
goseq_result <- sm(simple_goseq(sig_genes = test_genes, go_db = pombe_goids,
                                length_db = lengths))

head(goseq_result[["all_data"]])

##        category over_represented_pvalue under_represented_pvalue numDEInCat
## 344  GO:0005634               2.969e-43                        1         99
## 351  GO:0005730               1.054e-31                        1         33
## 137  GO:0003674               1.573e-27                        1         75
## 1236 GO:0042254               1.017e-23                        1         21
## 461  GO:0006364               2.634e-23                        1         20
## 353  GO:0005737               3.298e-22                        1         71
##      numInCat                term ontology    qvalue
## 344       576             nucleus       CC 5.143e-40
## 351        58           nucleolus       CC 9.131e-29
## 137       514  molecular_function       MF 9.083e-25
## 1236       30 ribosome biogenesis       BP 4.402e-21
## 461        25     rRNA processing       BP 9.125e-21
## 353       535           cytoplasm       CC 9.520e-20

goseq_result[["pvalue_plots"]][["mfp_plot_over"]]

goseq_result[["pvalue_plots"]][["bpp_plot_over"]]

test_genes <- updown_genes[["up_genes"]]
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
goseq_result <- sm(simple_goseq(sig_genes = test_genes, go_db = pombe_goids,
                                length_db = lengths))

head(goseq_result[["all_data"]])

##       category over_represented_pvalue under_represented_pvalue numDEInCat
## 643 GO:0008150               4.739e-48                        1        116
## 384 GO:0005829               7.894e-47                        1        120
## 353 GO:0005737               2.788e-46                        1        118
## 344 GO:0005634               1.047e-45                        1        121
## 137 GO:0003674               4.076e-42                        1        109
## 854 GO:0016020               9.518e-40                        1         97
##     numInCat               term ontology    qvalue
## 643      517 biological_process       BP 8.207e-45
## 384      551            cytosol       CC 6.836e-44
## 353      535          cytoplasm       CC 1.610e-43
## 344      576            nucleus       CC 4.532e-43
## 137      514 molecular_function       MF 1.412e-39
## 854      415           membrane       CC 2.747e-37

goseq_result[["pvalue_plots"]][["mfp_plot_over"]]

goseq_result[["pvalue_plots"]][["bpp_plot_over"]]

2.8 ClusterProfiler test

clusterProfiler really prefers an orgdb instance to use, which is probably smart, as they are pretty nice. Sadly, there is no pre-defined orgdb for pombe…

## holy crap makeOrgPackageFromNCBI is slow, no slower than some of mine, so who am I to complain.
if (! "org.Spombe.eg.db" %in% installed.packages()) {
  orgdb <- AnnotationForge::makeOrgPackageFromNCBI(
                                version = "0.1", author = "atb <abelew@gmail.com>",
                                maintainer = "atb <abelew@gmail.com>", tax_id = "4896",
                                genus = "Schizosaccharomyces", species = "pombe")
  ## This created the directory 'org.spombe.eg.db'
  devtools::install_local("org.Spombe.eg.db")
}
library(org.Spombe.eg.db)
## Don't forget to remove the terminal .1 from the gene names...
## If you do forget this, it will fail for no easily visible reason until you remember
## this and get really mad at yourself.
rownames(test_genes) <- gsub(pattern = ".1$", replacement = "", x = rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern = ".1$", replacement = "", x = pombe_goids[["ID"]])
cp_result <- simple_clusterprofiler(sig_genes = test_genes, do_david = FALSE, do_gsea = FALSE,
                                    de_table = all_combined$data[[1]],
                                    orgdb = org.Spombe.eg.db, orgdb_to = "ALIAS")
cp_result[["pvalue_plots"]][["ego_all_mf"]]
## Yay bar plots!

## Get rid of those stupid terminal .1s.
rownames(test_genes) <- gsub(pattern = ".1$", replacement = "", x = rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern = ".1$", replacement = "", x = pombe_goids[["ID"]])
tp_result <- sm(simple_topgo(sig_genes = test_genes, go_db = pombe_goids, pval_column = "limma_adjp"))

tp_result[["pvalue_plots"]][["mfp_plot_over"]]

tp_result[["pvalue_plots"]][["bpp_plot_over"]]

## Get rid of those stupid terminal .1s.
##rownames(test_genes) <- gsub(pattern = ".1$", replacement = "", x = rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern = ".1$", replacement = "", x = pombe_goids[["ID"]])
## universe_merge is the column in the final data frame when.
## gff_type is the field in the gff file providing the id, this may be redundant with
## universe merge, that is something to check on...
gst_result <- sm(simple_gostats(sig_genes = test_genes, go_db = pombe_goids, universe_merge = "id",
                                gff_type = "gene",
                                gff = "pombe.gff", pval_column = "limma_adjp"))
gst_result[["pvalue_plots"]][["mfp_plot_over"]]

gst_result[["pvalue_plots"]][["bpp_plot_over"]]

pander::pander(sessionInfo())

R version 4.0.3 (2020-10-10)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: splines, stats4, parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: GO.db(v.3.12.1), AnnotationDbi(v.1.52.0), GOstats(v.2.56.0), edgeR(v.3.32.1), lme4(v.1.1-26), Matrix(v.1.3-2), BiocParallel(v.1.24.1), variancePartition(v.1.20.0), fission(v.1.10.0), ruv(v.0.9.7.1), hpgltools(v.1.0), SummarizedExperiment(v.1.20.0), GenomicRanges(v.1.42.0), GenomeInfoDb(v.1.26.2), IRanges(v.2.24.1), S4Vectors(v.0.28.1), MatrixGenerics(v.1.2.1), matrixStats(v.0.58.0), Biobase(v.2.50.0) and BiocGenerics(v.0.36.0)

loaded via a namespace (and not attached): utf8(v.1.1.4), R.utils(v.2.10.1), tidyselect(v.1.1.0), RSQLite(v.2.2.3), htmlwidgets(v.1.5.3), grid(v.4.0.3), Rtsne(v.0.15), IHW(v.1.18.0), munsell(v.0.5.0), codetools(v.0.2-18), preprocessCore(v.1.52.1), statmod(v.1.4.35), withr(v.2.4.1), colorspace(v.2.0-0), Category(v.2.56.0), highr(v.0.8), knitr(v.1.31), rstudioapi(v.0.13), Vennerable(v.3.1.0.9000), robustbase(v.0.93-7), genoPlotR(v.0.8.11), labeling(v.0.4.2), slam(v.0.1-48), GenomeInfoDbData(v.1.2.4), lpsymphony(v.1.18.0), topGO(v.2.42.0), bit64(v.4.0.5), farver(v.2.0.3), rprojroot(v.2.0.2), vctrs(v.0.3.6), generics(v.0.1.0), xfun(v.0.21), BiocFileCache(v.1.14.0), fastcluster(v.1.1.25), R6(v.2.5.0), doParallel(v.1.0.16), locfit(v.1.5-9.4), bitops(v.1.0-6), cachem(v.1.0.4), DelayedArray(v.0.16.1), assertthat(v.0.2.1), scales(v.1.1.1), gtable(v.0.3.0), sva(v.3.38.0), rlang(v.0.4.10), genefilter(v.1.72.1), rtracklayer(v.1.50.0), lazyeval(v.0.2.2), selectr(v.0.4-2), broom(v.0.7.5), yaml(v.2.2.1), reshape2(v.1.4.4), GenomicFeatures(v.1.42.1), crosstalk(v.1.1.1), backports(v.1.2.1), qvalue(v.2.22.0), RBGL(v.1.66.0), tools(v.4.0.3), ggplot2(v.3.3.3), ellipsis(v.0.3.1), gplots(v.3.1.1), jquerylib(v.0.1.3), RColorBrewer(v.1.1-2), blockmodeling(v.1.0.0), Rcpp(v.1.0.6), plyr(v.1.8.6), progress(v.1.2.2), zlibbioc(v.1.36.0), purrr(v.0.3.4), RCurl(v.1.98-1.2), BiasedUrn(v.1.07), ps(v.1.5.0), prettyunits(v.1.1.1), openssl(v.1.4.3), ggrepel(v.0.9.1), colorRamps(v.2.3), magrittr(v.2.0.1), data.table(v.1.14.0), openxlsx(v.4.2.3), SparseM(v.1.81), goseq(v.1.42.0), pkgload(v.1.2.0), hms(v.1.0.0), evaluate(v.0.14), xtable(v.1.8-4), pbkrtest(v.0.5-0.1), XML(v.3.99-0.5), gridExtra(v.2.3), testthat(v.3.0.2), compiler(v.4.0.3), biomaRt(v.2.46.3), tibble(v.3.0.6), KernSmooth(v.2.23-18), crayon(v.1.4.1), minqa(v.1.2.4), R.oo(v.1.24.0), htmltools(v.0.5.1.1), mgcv(v.1.8-34), corpcor(v.1.6.9), tidyr(v.1.1.2), geneplotter(v.1.68.0), DBI(v.1.1.1), geneLenDataBase(v.1.26.0), dbplyr(v.2.1.0), MASS(v.7.3-53.1), rappdirs(v.0.3.3), boot(v.1.3-27), ade4(v.1.7-16), readr(v.1.4.0), cli(v.2.3.1), quadprog(v.1.5-8), R.methodsS3(v.1.8.1), pkgconfig(v.2.0.3), GenomicAlignments(v.1.26.0), plotly(v.4.9.3), xml2(v.1.3.2), foreach(v.1.5.1), annotate(v.1.68.0), bslib(v.0.2.4), XVector(v.0.30.0), AnnotationForge(v.1.32.0), rvest(v.0.3.6), EBSeq(v.1.30.0), stringr(v.1.4.0), digest(v.0.6.27), graph(v.1.68.0), Biostrings(v.2.58.0), rmarkdown(v.2.7), GSEABase(v.1.52.1), directlabels(v.2021.1.13), curl(v.4.3), Rsamtools(v.2.6.0), gtools(v.3.8.2), nloptr(v.1.2.2.2), lifecycle(v.1.0.0), nlme(v.3.1-152), jsonlite(v.1.7.2), desc(v.1.2.0), viridisLite(v.0.3.0), askpass(v.1.1), limma(v.3.46.0), fansi(v.0.4.2), pillar(v.1.5.0), lattice(v.0.20-41), fastmap(v.1.1.0), httr(v.1.4.2), DEoptimR(v.1.0-8), survival(v.3.2-7), glue(v.1.4.2), zip(v.2.1.1), fdrtool(v.1.2.16), iterators(v.1.0.13), Rgraphviz(v.2.34.0), pander(v.0.6.3), bit(v.4.0.4), stringi(v.1.5.3), sass(v.0.3.1), blob(v.1.2.1), DESeq2(v.1.30.1), caTools(v.1.18.1), memoise(v.2.0.0) and dplyr(v.1.0.4)